I would find it kind of satisfying if Waymo solidly won the self-driving race. It would be a triumph of solid engineering over unreliable "good-enough" solutions.
You need a mass amount of data to train on. How do you get that? Its an engineering or a business problem, depending how how you look at it - how do you get a ton of people driving for you?
Tesla has the better business model here. That leads to more data, which is the most important factor in training.
Now, google is orders of magnitude bigger and had a huge head start, perhaps they brute force their way to the lead. But being more conservative != better engineering. You could philosophically disagree with many things Tesla is doing, but in terms of solving self driving cars I think they have the better strategy that gives their engineers the data they need.
Tesla is in the real world recording every car they sell through vastly more dynamic scenario/terrain. That’s smart engineering.
Isn't being conservative better when your technology can kill people?
You're just showcasing the immaturity of most software "engineers", yourself included.
First, you made a huge assumption. I never said I approve of how they are doing it. Engineering at the highest level encompasses morality, obviously.
But you wooshed real hard on the context of this thread in your rush to get that sweet feeling of moral superiority.
I’ll rewind it for you.
I was replying to someone claiming they hope Google delivers self-driving first because they see them as executing better at the engineering portion of doing so.
Do you see now? If they had said they see them as engineering more ethically and so they hope they win, fine. But they didn’t. Read it again.
The context is: is Google’s engineering strategy going to deliver self-driving first?
I replied to that. I even called out that you can totally disagree on moral grounds, and I do disagree with much of what they do, so it’s funny to call me out on something I was leaving purposely open as a further topic to discuss.
But the context was who would deliver self-driving faster, due strictly to their engineering strategy, I made the case that real world data trumps artificial data. And it does.
I'm pretty sure "don't kill user" is not only an engineering requirement, but the most important one.
Who at Tesla was held liable for designing a system that failed and resulted in death? Who at Tesla lost their licenses in the wake of deaths caused by their self-driving systems? Where is the culture of ensuring that their cars don't kill anyone again?
I’ll be the first to criticize Tesla for the shoddy, dangerously worded rollout of Autopilot. Totally. Has it saved more lives than lost? Who knows.
But to argue that googles more conservative approach should somehow deliver FSD faster is just a contradiction. Perhaps they brute force it like I said already. But the fast rollout by Tesla == more dangerous == more data. If you’re talking engineering delivery time, then yea Tesla is taking a calculated risk of getting much more data much faster. They should be able to deliver faster, engineering-wise.
If we are talking an eventual 10% reduction waiting just 1 year costs ~130,000 lives. On the other hand if mistakes slows long term adoption that’s also harmful.
PS: Adoption curves etc are also import, but that’s what I mean by mistakes slowing adoption.
And yet there is no evidence of these assertions other than wishful thinking.
There is no evidence that self-driving systems are safer than humans, so there is no reason to treat them as if they are. In fact, compared to other luxury vehicles, Tesla vehicles are involved in more accidents.
The medical world is extremely conservative. The FDA has incredibly high standards to meet, thankfully, and demands ample scientific evidence for the medicines and treatments it approves.
"Move fast and break things" doesn't exist in the medical world, but it does exist outside of it in the form of unregulated supplements and illegal procedures and distribution of medication. People are routinely made into victims of this market just so someone can make a quick buck.
Anyway, move fast and break things is ingrained into the core of the medical profession when there is an unmet medical need.
The FDA for example has: Accelerated Approval, Fast track designation, Breakthrough therapy designation, and Priority Review all designed to speed things up when the benefits are significant.
They don’t apply for new pain medication because the risk vs reward is very different. 2014: https://www.fda.gov/media/94063/download
PS: I think we can agree that in terms of car safety there is clearly a large unmet need. That’s not to say self driving is the solution, just that it’s a serious contender.
If I offer you an untested unreliable cancer cure, on the grounds that moving slowly kills more in the long run, I'm likely to make things worse by putting out a non-cure. We could do some really fucked up human experimentation to save more lives in the long run, but that's not the ethics we've collectively agreed on.
Informed consent is a really big deal, but not a blanket exception for anything. Compensated participation for example is tricky. That’s the kind of issues that make medical ethics a complex subject and there are often difficult choices to be made.
Do you have a source for this claim?
Federated learning is a lot like distributed training of a neural network.
The trouble with distributed training is that it's not as fast to converge as simply running more training steps. It is basically like increasing the batch size.
Also, it sounds like a general hassle for Tesla to use federated learning. I believe they need to be carefully auditing and labelling their training data for almost all their tasks. Perhaps some like depth-estimation don't require labelled data.
"You are the product."
There is indeed way more data captured than can be sent back, or even stored long term on the car.
It might be possible to handle steering, navigation, obstacle and accident avoidance purely with non-ML approaches. Then ML could be left to less safety critical features such as predicting the types of obstacles surrounding it, and their likely next moves. LIDAR (gasp!) might even help make the problem much easier.
So no, I don't think that just saying you have more data is the end of the story. It's certainly AN approach, and maybe a good one, but I don't think you can conclude their engineering is better because of it.
Tesla wins got having the most data about driving in the Bay Area. And not so much for driving anywhere else, or in bad weather, or on bad roads, or on highways where trucks may cross both lanes of traffic.
Now I see the problem of self driving is now just data and and computation, each of which grows by bounds every month...
can't believe the amount of 'wishful thinking can replace science' that Tesla pushes out
and now he has gotten into Neuralink and chips in the brains of pigs stupidness
Tesla could win, with shoddy engineering, if they can convince the public and government (using the classic reality distortion field :) ) their self driving tech is ready for prime time (when, for the sake of argument, presume it's not ready).
They could then use the investment dollars generated to take it to the next level and perhaps make it solid over time -- but it could take a long time.
In the mean time, the public was effectively deceived as to the true safety level of the product. I would prefer if the opposite happened instead.
marketing, brand image, any of the other dozen things that mask bad engineering quality and sell the customer on some superficial thing.
Not just in cars but a ton of consumer products customers can easily neglect engineering quality in favour of some gimmick.
Hell even developers constantly choose convenient or shiny technologies over ones that are robust or error free.
A crowded parking lot of a mall can be challenging for a beginner human driver, it's an absolute AI nightmare as there can be totally unexpectedly moving cars and pedestrians at any place and time. And then {rain,fog,snow} messes with LIDAR and you have a half blind (at best) system trying to navigate an extremely challenging environment.